How Will We Know When We Have Achieved Universal Health Coverage?

Transcription

1 How Will We Know When We Have Achieved Universal Health Coverage? The Newly Revamped Health Equity and Financial Protection Indicators (HEFPI) Database Adam Wagstaff Research Manager, Development Research Group + Patrick Eozenou, Sven Neelsen & Marc Smitz + previously Caryn Bredenkamp & Leander Buisman + even further back Davidson Gwatkin, Alex Ergo, et al.

2 What s UHC? Everyone, irrespective of their means, receives the health services they need without suffering financial hardship AN ASPECT OF HEALTH EQUITY (HE) FINANCIAL PROTECTION (FP) We need data on HE and FP to know how close we are to achieving UHC

3 18 Years of World Bank Data on Health Equity and Financial Protection countries all developing countries 56 countries all developing countries 109 countries incl. some highincome 183 countries goal is global coverage 42 surveys all DHS 95 surveys all DHS 285 surveys DHS, MICS & WHS 1,603 surveys DHS, MICS, WHS, LSMS, HBS, etc. 34 indicators all services or outcomes 115 indicators all services or outcomes 73 indicators incl. 4 FP indicators 42 indicators incl. more NCD and FP indicators Focus on equity in MDG indicators Focus on equity in MDG indicators Not just MDG indicators some NCD and FP indicators Levels of and equity in MDG and SDG indicators, incl. FP

4 January-00 July-00 January-01 July-01 January-02 July-02 January-03 July-03 January-04 July-04 January-05 July-05 January-06 July-06 January-07 July-07 January-08 July-08 January-09 July-09 January-10 July-10 January-11 July-11 January-12 July-12 January-13 July-13 January-14 July-14 January-15 July-15 January-16 July-16 January-17 July-17 January-18 July-18 From the 2000 database to the 2018 database No. household surveys analyzed FP SC

5 Geo coverage -> From the 2000 database

6 to the 2018 database

7 Topic coverage -> From the 2000 database

8 to the 2018 database

9 Survey coverage -> From the 2000 database

10 to the 2018 database

11 Inequalities between and within countries The case of full immunization 4 Chad; Cote d'ivoire; Ghana; Kenya; Mali; Poorest quintile Richest quintile

12 Changes in inequalities from the 1990s to the 2010s Selected MNCH coverage indicators Poorest quintile 1990s Poorest quintile 2010s Richest quintile 1990s Richest quintile 2010s ANC Immunization SBA Treat Acute Resp. Infection Treat Diarrhea Met Need for Family Planning countries with data for 1990s, 2000s and 2010s

13 Inequalities across countries in coverage indicators High income Upper middle inc Lower middle inc Low income Treat Diarrhea IP admission Mammogram PAP smear ANC Treat ARI Full immunization SBA Inpatient admission in last year as % of equivalent WHO benchmark (~9%)

14 Inequalities across countries in coverage indicators High income Upper middle inc Lower middle inc Low income Thailand Zimbabwe Treat Diarrhea IP admission Mammogram PAP smear ANC Treat ARI Full immunization SBA Inpatient admission in last year as % of equivalent WHO benchmark (~9%)

15 Inequalities within countries in non-mdg indicators 12% 10% 8% 6% 4% 2% Inpatient admission last 12 months PAP smear last 5 yrs, women age % 80% 70% 60% 50% 40% 30% 20% 10% 0% High income countries Low income countries 0% High income countries Low income countries Q1 (poorest) Q2 Q3 Q4 Q5 (richest) Q1 (poorest) Q2 Q3 Q4 Q5 (richest)

16 Financial protection: catastrophic expenses Percent incurring expenses more than 10% of consumption/income Trends in selected countries 35% 30% 25% 20% 15% Georgia 10% 5% 0% Mexico USA Indonesia Inequalities across income quintiles Median across 35 upper middle-inc. & high-inc. countries 9% 8% 7% 6% 5% 4% 3% 2% 1% 0% Q1 (lowest income) Q2 Q3 Q4 Q5 (highest income)

17 Using the database to track progress towards UHC Service Coverage (SC) Financial Protection (FP) Prevention Antenatal care Immunization Breast cancer screening Cervical Cancer Screening Treatment Skilled Birth Attendant Treatment for ARI Treatment for Diarrhea Inpatient admission last 12m CATA 10 UHC Index 12.5% weight 37.5% weight 50% weight Indicators are combined as a weighted geometric mean. CATA is rescaled as 100-CATA. Ideally, all indicators should be adjusted for inequality

18 SC Service coverage (SC) vs. financial protection (FP) Data averaged across multiple years; years vary by country LUX SWE 2006 FIN FRA 2005 GBR 2005 AUS USA 2006 ITA BEL 2007 SVN SVK HUN 2004 CAN 2007 CHE KOR 2009 ISR 2005 LTU 2005 POL 2007 CYP GRC 2009 PRT IRL 2006 LVA MLT DNK 2006 CZE 2006 HRV 2005 ZAF RUS LKA 2004 GTM 2007 ESP 2006 UKR 2007 MNG 2007 MYS 2003 EST 2002 THA 2009 MUS 2006 KAZ 2006 JPN 2008 BIH 2005 SRB 2007 NAM 2006 URY 2003 CRI 2009 MEX ECU MDA 2007 ZWE 2007 PHL 2007 PRY COG PER 2010 COM 2008 VNM 2005 DOM 2005 COL 2008 ARG 2007 CHN 2003 NIC 2005 BRA 2006 CHL 2007 GHA 2007 AZE 2004 KEN 2004 SEN 2007 PAK 2007 BFA 2006 LAO 2004 TCD 2003 ARM 2007 MRT 2008 MAR 2001 TJK 2006 IND 2005 SWZ 2006 NPL 2007 GEO 2005 IDN 2007 MLI 2006 MMR 2010 BGD 2007 ETH FP-CATA 10% High income Lower middle income Upper middle income Low income

19 SC Putting SC and FP together in a UHC Index Data averaged across multiple years; years vary by country LUX SWE 2006 FIN FRA 2005 GBR 2005 AUS USA 2006 ITA BEL 2007 SVN SVK HUN 2004 CAN 2007 CHE KOR 2009 ISR 2005 LTU 2005 POL 2007 CYP GRC 2009 PRT IRL 2006 LVA MLT DNK 2006 CZE 2006 HRV 2005 UHC=90 CHL 2007 UHC=80 ZAF RUS LKA 2004 GTM 2007 ESP 2006 UKR 2007 MNG 2007 MYS 2003 EST 2002 THA 2009 MUS 2006 KAZ 2006 JPN 2008 BIH 2005 SRB 2007 NAM 2006 URY 2003 CRI 2009 MEX ECU MDA 2007 ZWE 2007 PHL 2007 PRY COG PER 2010 COM 2008 VNM 2005 DOM 2005 COL 2008 ARG 2007 CHN 2003 NIC 2005 BRA 2006 UHC=70 UHC=60 GHA 2007 AZE 2004 KEN 2004 SEN 2007 PAK 2007 BFA 2006 LAO 2004 TCD 2003 ARM 2007 MRT 2008 MAR 2001 TJK 2006 IND 2005 SWZ 2006 NPL 2007 GEO 2005 UHC=50 IDN 2007 MLI 2006 MMR 2010 BGD 2007 ETH FP-CATA 10% High income Lower middle income Upper middle income Low income

20 Putting SC and FP together in a UHC Index Data averaged across multiple years; years vary by country UHC Index No data

21 Explore the data using the HEFPI Data Visualizer Hands-on class starting right now! Grab a lunch box Head to Columbia 2 Bring your laptop or tablet

22 HEFPI Data Visualizer and HEFPI Country Dataset Data Visualizer Dataset (coming soon) Check back often updates happening regularly